Color retrieval is often based on retrieving images from a set of images. Typically, whole images are used. Occasionally, images are pre-segmented and then retrieval of objects is performed. Only recently, has work been performed to study the retrieval of moving objects from video based on color information. This work has been preliminary and has been limited by issues of color constancy, including changing lighting conditions, changing orientation of surfaces with respect to lighting, and the broad complexity of objects.
Color retrieval is typically based on predefined familiar colors, based on a query example or on color categorization derived from the color search space. The color retrieval often requires a query input or unsupervised learning, which both consume resources. Further, the requirement of having training data, specifically data which is manually or semi-automatically labeled for each set of cameras, views, lighting conditions or types of objects, is also inefficient.
These various color retrieval methodologies are also not adaptive. In order to work on a new camera, view, lighting condition or class of objects, there is an implicit requirement for additional training data to update the classification.
Additionally, color retrieval methods employ complex color distribution information to match images, regions or objects. For example, spatial color histograms are used to describe the color of complex objects. However, there are shortcomings with these methods because they do not adequately address several issues. The first is the issue of color constancy. People perceive an object to be the same color across a wide range of illumination conditions. However, the actual pixels of the object, while perceived to be the same color, may have values which range across the color spectrum depending on the lighting conditions. Second, moving objects extracted from video are not perfectly segmented from the background. Shadows are often part of the object and errors are caused in the segmentation due to the similarity of the object and background model. Lastly, complex objects are not predominately one color. Certain aspects of objects are of interest to the human and these depend on the type of object and application. All of these deficiencies make it difficult to select moving objects based on familiar color descriptors.
In view of the foregoing, a need exists to overcome one or more of the deficiencies in the related art.